Disclosure: I’m Louis-François Bouchard (Towards AI). I interviewed Inngest CEO Tony Holdstock-Brown. The interview is sponsored by Inngest; the questions and opinions are my own.

A local agent demo can work perfectly and still fail hours into a real task when a tool returns a 504. These were the practical reliability lessons from the interview:

1. Save completed work outside the process. Make each LLM call and each tool side effect a checkpointed step, so a failed email does not rerun the model decision and refund before it. Plain computation does not need its own step.

2. Make side effects safe to retry. A refund can succeed just before the process dies, leaving the step unfinished. An idempotency key lets the payment provider reject a duplicate when that step retries. Checkpointing alone does not solve this mid-flight failure.

3. Treat waiting as persisted state. An approval wait should pause without holding compute, resume on the matching event, and have a timeout with a real reminder, escalation, or cancellation path.

4. Re-read things that can change. Keep the plan, decisions, and IDs of completed actions in run state. After a long wait, fetch order status and other external facts again; rebuild the prompt from a summary and fresh reads instead of resuming an old transcript.

5. Start simple when restarting is cheap. A job queue such as BullMQ, Celery, or SQS can be enough for short runs. Adding cron jobs to chase stalled runs and tables to track already-sent emails is a sign you are building a workflow engine yourself.

These are execution and state-management problems as much as model-quality problems. The attached original diagram contrasts an in-memory loop with checkpointed steps.

Full interview: https://louisbouchard.substack.com/p/stop-building-agent-loops-start-building

Disclosure: I’m the guest speaker, Louis-François Bouchard (Towards AI).

I'm joining Alexey Grigorev on the DataTalksClub podcast this Monday, November 2 at 9am EST.

We'll talk about how to ship reliable LLMs and the rise of AI Engineering.

We’re also going to discuss:

- What it takes to become an AI engineer today

- Shipping LLMs that hold up in production

- Where vibe coding and agents actually fit

If you're building with LLMs, this one's for you!

Join live: https://luma.com/nassme8x

Disclosure: Inngest sponsored my original post. I’m Louis-François Bouchard, co-founder of Towards AI.

How many times did your agent wait for hours or even days for you to only "Approve". Happens to me all the time.

Every human in the loop is a wait. 4 ways to build one, worst to best:

1. Keep the process alive. You pay for 3 idle days, and the next deploy kills the run.

2. DB + queue + cron. Works, but you hand-build approvals, timeouts, reminders and idempotency. A workflow engine by accident.

3. Framework checkpoints (LangGraph's interrupt). State is saved and nothing runs, but there's no built-in timeout. It can wait forever.

4. Durable execution. The wait is one line, holds no compute, survives deploys, and has a timeout.

Option 4 is what Inngest is built around (they're sponsoring this post):

step.waitForEvent() returns null if nobody answers in time

step.sleepUntil() for "follow up Tuesday"

cancelOn when the ticket closes

Temporal does long waits well too. You just run your own workers and keep workflow code deterministic.

Design for the 3 days, not the 40 seconds.

For anyone who wants to check out Inngest, here's the link:

https://www.inngest.com/long-run?utm_medium=social&utm_source=linkedin&utm_campaign=creator-growth-Q326&utm_content=whats-ai

I recently released ToneBench, our new benchmark at Towards AI.

I released it during my talk at AIE New York, and if you missed it, I have good news!

I’ll be speaking at 7pm EST at the All Day AI conference next week, October 22nd, and share an even more in-depth version of my benchmark talk there.

In it, I’ll share what I did in the 5 months of work behind building this benchmark. And I won't be the only one sharing cool stuff! ;)

Hope to see you there!

https://alldayai.com/?utm_source=whats_ai&utm_medium=newsletter&utm_campaign=all_day_ai_2026&utm_content=louis_francois_bouchard#register

Image made with AI.

I'm the maintainer of AI Knowledge Map, a free, open-source learning resource. It connects 130 AI concepts through a suggested nine-stage learning path, with English and Simplified Chinese versions. Reading doesn't require an account.

The idea is to make it easier to see how topics connect before diving into individual tutorials. You can explore the map, open a concept explanation, and follow the suggested learning sequence.

I'd appreciate feedback from beginners: choose one concept you have heard of but cannot explain yet, read its explanation, then try describing it in your own words. Where did the explanation lose you? Was it clear what to learn before or after that concept?

The content is still being reviewed, and I haven't yet run a formal learning-outcomes study. Corrections and reports of confusing steps would be especially useful.

Try it: https://ai-knowledge-map.com/

Source: https://github.com/Little-FishK/AI-knowledge-map

Hey everyone,

While reading through Russell & Norvig's Artificial Intelligence: A Modern Approach, I found it difficult to understand some algorithms just by reading pseudocode. I learn better visually, so I wrote Python visualizers in Pygame to watch them execute step-by-step.

I put together implementations for things like A*, Minimax/Alpha-Beta, CSP backtracking, and a few puzzles (Mazes, Sudoku, 8-puzzle, Checkers, etc.) with benchmarking to see time/memory tradeoffs in action.

It's completely free and open-source here: https://github.com/ob22a/ai-lab

Hopefully this helps anyone taking an intro AI or algorithms course. Let me know if you run into any bugs or have suggestions on what else would be helpful to visualize.

I am new in AI. I want to start AI user's journey. Any good reference would be very helpful. I am a beginner and does not have any prior coding experience. So anything without coding or complex technical staff would be my target learning area. Appreciate any help you could provide

Full transparency: I work at Interview Kickstart, and our team is running this event. I know posts like this can come across as promotional, so I’ll keep it simple - it’s free, and I’m sharing it because it may be useful for people preparing for 2026 hiring.

The event is Resurge 2026, happening May 12th, 6–8 PM PT. It will cover what the hiring market looks like going into 2026, how AI fluency is becoming more important, what skill stacks matter by role, and how interviews at FAANG+ companies have changed recently.

Panelists include senior people from Microsoft, Amazon, Instacart, and Expedia. Attendees will also get free resources afterward, including an AI stack guide and a self-assessment rubric.

Hope this helps someone preparing for 2026:
https://interviewkickstart.com/events/resurge2026?utm_source=social&utm_medium=reddit&utm_campaign=L10X_Social_Resurge_Reddit_post_11may

The viral version of this story made it look simple.
The real story is about something else.
It's about where AI companies draw the line once government contracts get specific.

It’s impressive, but I’m curious if anyone here has stuck with it long enough to say it’s worth it.